Real-Time Emission Level Determination of Power Plants

The present disclosure relates to emission profiling and, more particularly, to estimating real-time emissions of power plants.

2 x x In recent years, rapid industrialization and rising global energy demands have led to a significant increase in the number of power plants and industrial facilities. While these facilities are crucial for economic development, they also contribute to air pollution and greenhouse gas emissions. Pollutants such as carbon dioxide (CO), sulfur oxides (SO), nitrogen oxides (NO), and particulate matter have been directly linked to environmental issues including global warming, acid rain, and respiratory health problems.

This growing concern over environmental pollution and its impact on climate change has intensified the need for effective monitoring and control of greenhouse gas emissions. Accurate measurement of greenhouse gas emissions is utilized for ensuring compliance with environmental regulations, implementing pollution control measures, and developing strategies to mitigate their harmful effects on the environment.

According to an embodiment of the present disclosure, a computer-implemented method for determination of real-time emission levels of power plants is described. The computer-implemented method includes receiving, by a computer, image data associated with a plurality of images of a geographical region. The geographical region includes a specific power plant. The image data indicates emission data associated with a gas. The computer-implemented method further includes generating, by the computer using a first artificial intelligence (AI) model, a first emission level associated with the gas based on the image data. The first emission level indicates a total amount of the gas present in the geographical region. The computer-implemented method further includes generating, by the computer using a second AI model, a second emission level based on the first emission level. The second emission level is associated with an emission of the gas by the specific power plant within a first predefined time period based on the first emission level. The computer-implemented method further includes generating, by the computer, a third emission level based on the second emission level. The third emission level is associated with an emission of the gas by the specific power plant within each of a plurality of second predefined time periods. The first predefined time period includes the plurality of the second predefined time periods. The computer-implemented method further includes determining, by the computer using a third AI model, calibration factor data based on the third emission level associated with each time period of the plurality of second predefined time periods, the calibration factor data is associated with the emission of the gas by the specific power plant within the first predefined time period. The calibration factor data includes a plurality of calibration values corresponding to a plurality of time instants within the first predefined time period. The computer-implemented method further includes estimating, by the computer, a fourth emission level based on the calibration factor data, the fourth emission level is associated with the emission of the gas by the specific power plant at each time instant of the plurality of time instants.

According to an embodiment of the present disclosure, a computer system for determination of real-time estimation level of the specific power plant is described. The computer system includes a processor set, one or more computer-readable storage media, and program instructions stored on the one or more computer-readable storage media, the program instructions executable by the processor set to cause the processor set to receive image data associated with a plurality of images of a geographical region. The geographical region includes a specific power plant. The image data indicates emission data associated with a gas, location data associated with the plurality of images, and a timestamp associated with the plurality of images. Further, the program instructions cause the processor set to reconfigure a first artificial intelligence (AI) model based on the image data and a loss function associated with the image data. Further, the program instructions cause the processor set to generate a first emission level associated with the gas based on an application of the first AI model on the image data. The first emission level indicates the total amount of the gas present in the geographical region. Further, the program instructions cause the processor set to generate a second emission level based on an application of a second AI model on the first emission level, the second emission level is associated with an emission of the gas by the specific power plant within a first predefined time period. Further, the program instructions cause the processor set to generate a third emission level based on the second emission level, the third emission level is associated with the emission of the gas by the specific power plant within each time period of a plurality of second predefined time periods. The first predefined time period includes the plurality of the second predefined time periods. Further, the program instructions cause the processor set to determine calibration factor data based on an application of a third AI model on the third emission level associated with each time period of the plurality of second predefined time periods, the calibration factor data is associated with the emission of the gas by the specific power plant within the first predefined time period. The calibration factor data includes a plurality of calibration values that correspond to a plurality of time instants within the first predefined time period. Further, the program instructions cause the processor set to estimate a fourth emission level based on the calibration factor data, the fourth emission level is associated with the emission of the gas by the specific power plant at each time instant of the plurality of time instants.

According to an embodiment of the present disclosure, a computer program product for estimation of emission levels associated with emission of a gas by power plants is described. The computer program product includes one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media to perform operations including receiving image data associated with a plurality of images of a geographical region. The geographical region includes a specific power plant. The image data indicates emission data associated with the gas. The operations further include generating a first emission level associated with the gas based on an application of a first artificial intelligence (AI) model on the image data. The first emission level indicates a total amount of the gas present in the geographical region. The operations further include generating a second emission level based on an application of a second AI model on the first emission level, the second emission level is associated with an emission of the gas by the specific power plant within a first predefined time period. The operations further include generating a third emission level based on the second emission level. The third emission level is associated with an emission of the gas by the specific power plant within each time period of a plurality of second predefined time periods. The first predefined time period includes the plurality of the second predefined time periods. The operations further include determining calibration factor data based on an application of a third AI model on the third emission level associated with each time period of the plurality of second predefined time periods, the calibration factor data is associated with the emission of the gas by the specific power plant within the first predefined time period. The calibration factor data includes a plurality of calibration values that correspond to a plurality of time instants within the first predefined time period. The operations further include estimating a fourth emission level based on the calibration factor data. The fourth emission level is associated with the emission of the gas by the specific power plant at each time period of the plurality of time instants.

Additional technical features and benefits are realized through the techniques of the present disclosure. Embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed subject matter. For a better understanding, refer to the detailed description and to the drawings.

According to an embodiment of the present disclosure, a computer-implemented method for the determination of real-time emission levels of power plants is described. The computer-implemented method includes receiving, by a computer, image data associated with a plurality of images of a geographical region. The geographical region includes a specific power plant, and the image data indicates emission data associated with a gas. The computer-implemented method further includes generating, by the computer using a first artificial intelligence (AI) model, a first emission level associated with the gas based on the image data. The first emission level indicates a total amount of the gas present in the geographical region. The computer-implemented method further includes generating, by the computer using a second AI model, a second emission level based on the first emission level. The second emission level is associated with an emission of the gas by the specific power plant within a first predefined time period based on the first emission level. The computer-implemented method further includes generating, by the computer, a third emission level based on the second emission level. The third emission level is associated with an emission of the gas by the specific power plant within each of a plurality of second predefined time periods. The first predefined time period includes the plurality of second predefined time periods. The computer-implemented method further includes determining, by the computer using a third AI model, calibration factor data based on the third emission level associated with each time period of the plurality of second predefined time periods, the calibration factor data is associated with the emission of the gas by the specific power plant within the first predefined time period. The calibration factor data includes a plurality of calibration values corresponding to a plurality of time instants within the first predefined time period. The computer-implemented method further includes estimating, by the computer, a fourth emission level based on the calibration factor data, the fourth emission level is associated with the emission of the gas by the specific power plant at each time instant of the plurality of time instants.

In an embodiment, the first AI model is a geospatial foundational model (GFM).

In an embodiment, the image data indicates location data associated with the plurality of images and a timestamp associated with the plurality of images, the computer-implemented method further includes reconfiguring, by the computer, the first AI model based on the image data and a loss function associated with the image data. The computer-implemented method further includes determining, by the computer using the first AI model, profile data associated with the specific power plant based on the image data. The profile data includes at least one of a type or a segmented area within the plurality of images associated with the specific power plant.

In an embodiment, the loss function is a morphological loss function. The morphological loss function is based on a structure of the specific power plant within the plurality of images.

In an embodiment, the computer-implemented method further includes inputting, by the computer to the third AI model, the profile data associated with the specific power plant. The computer-implemented method further includes inputting, by the computer to the third AI model, the third emission level within each time period of the plurality of second predefined time periods. The computer-implemented method further includes receiving, by the computer, ground truth emission data associated with the specific power plant. The computer-implemented method further includes inputting, by the computer to the third AI model, the ground truth emission data. The computer-implemented method further includes determining, by the computer using the third AI model, the calibration factor data based on the profile data, the third emission level, and the ground truth emission data.

In an embodiment, the computer-implemented method further includes receiving, by the computer, power plant data associated with each power plant of a plurality of power plants. The power plant data comprises imagery data of a geographical location associated with each power plant of the plurality of power plants. The plurality of power plants is inclusive or exclusive of the specific power plant. The computer-implemented method further includes identifying, by the computer, a set of power plants from the plurality of power plants based on at least one similarity criterion between the power plant data associated with each power plant of the plurality of power plants and the profile data. The computer-implemented method further includes estimating, by the computer, emission data associated with the emission of the gas by each power plant of the set of power plants based on the calibration factor data.

In an embodiment, the computer-implemented method further includes receiving, by the computer, grid topology data associated with a power grid. The power grid is supplied by at least the specific power plant. The computer-implemented method further includes receiving, by the computer, power consumption data associated with a load. The load is connected to the power grid. The computer-implemented method further includes determining, by the computer using a fourth AI model, load emission data based on the grid topology data, the power consumption data, and the fourth emission level associated with the emission of the gas by the specific power plant at each time instant of the plurality of time instants. The load emission data is associated with the emission of the gas by the load.

In an embodiment, the power grid is connected to one or more power plants including the specific power plant. The computer-implemented method further includes determining, by the computer using the fourth AI model, distribution data associated with the load based on the grid topology data and the power consumption data. The distribution data indicates a distribution of a total amount of power consumed by the load over each power plant of the one or more power plants connected to the power grid. The computer-implemented method further includes determining, by the computer using the fourth AI model, the load emission data based on the distribution data and the fourth emission level associated with the emission of the gas by the specific power plant at each time instant of the plurality of time instants.

In an embodiment, the computer-implemented method further includes generating, by the computer, the third emission level using a dispersion model. The dispersion model is calibrated based on one or more environmental factors associated with the geographical region.

2 2 2 In an embodiment, the gas corresponds to at least one of Carbon dioxide (CO), Nitrogen dioxide (NO), or Sulphur dioxide (SO).

According to an embodiment of the present disclosure, a computer system for determination of real-time emission levels of power plants is described. The computer system includes a processor set, one or more computer-readable storage media, and program instructions stored on the one or more computer-readable storage media executable by the processor set to cause the processor set to receive image data associated with a plurality of images of a geographical region. The geographical region includes a specific power plant. The image data indicates emission data associated with a gas, location data associated with the plurality of images, and a timestamp associated with the plurality of images. Further, the program instructions cause the processor set to reconfigure a first artificial intelligence (AI) model based on the image data and a loss function associated with the image data. Further, the program instructions cause the processor set to generate a first emission level associated with the gas based on an application of the first AI model on the image data. The first emission level indicates the total amount of the gas present in the geographical region. Further, the program instructions cause the processor set to generate a second emission level based on an application of a second AI model on the first emission level, the second emission level is associated with an emission of the gas by the specific power plant within a first predefined time period. Further, the program instructions cause the processor set to generate a third emission level based on the second emission level, the third emission level is associated with the emission of the gas by the specific power plant within each time period of a plurality of second predefined time periods. The first predefined time period includes the plurality of the second predefined time periods. Further, the program instructions cause the processor set to determine calibration factor data based on an application of a third AI model on the third emission level associated with each time period of the plurality of second predefined time periods, the calibration factor data is associated with the emission of the gas by the specific power plant within the first predefined time period. The calibration factor data includes a plurality of calibration values that correspond to a plurality of time instants within the first predefined time period. Further, the program instructions cause the processor set to estimate a fourth emission level based on the calibration factor data, the fourth emission level is associated with the emission of the gas by the specific power plant at each time instant of the plurality of time instants.

In an embodiment, the first AI model is a geospatial foundational model (GFM).

In an embodiment, the loss function is a morphological loss function. The morphological loss function is based on a structure of the specific power plant within the plurality of images.

In an embodiment, the program instructions cause the processor set to determine profile data associated with the specific power plant based on an application of the first AI model on the image data. The profile data comprises at least one of a type, or a segmented area within the plurality of images associated with the specific power plant.

In an embodiment, the program instructions cause the processor set to input the profile data associated with the specific power plant to the third AI model. The program instructions cause the processor set to input the third emission level within each time period of the plurality of second predefined time periods to the third AI model. The program instructions cause the processor set to receive ground truth emission data associated with the specific power plant. The program instructions cause the processor set to input the ground truth emission data to the third AI model. The program instructions cause the processor set to determine the calibration factor data based on the application of the third AI model on the profile data, the third emission level, and the ground truth emission data.

In an embodiment, the program instructions cause the processor set to receive power plant data associated with each power plant of a plurality of power plants. The power plant data comprises imagery data of a geographical location associated with each power plant of the plurality of power plants. The plurality of power plants is inclusive or exclusive of the specific power plant. The plurality of power plants is inclusive or exclusive of the specific power plant. The program instructions cause the processor set to identify a set of power plants from the plurality of power plants based on at least one similarity criterion between the power plant data associated with each power plant of the plurality of power plants and the profile data. The program instructions cause the processor set to estimate emission data associated with the emission of the gas by each power plant of the set of power plants based on the calibration factor data.

In an embodiment, the program instructions cause the processor set to receive grid topology data associated with a power grid. The power grid is supplied by at least the specific power plant. The program instructions cause the processor set to receive power consumption data associated with a load. The load is connected to the power grid. The program instructions cause the processor set to determine load emission data based on an application of a fourth AI model on the grid topology data, the power consumption data, and the fourth emission level associated with the emission of the gas by the specific power plant at each time instant of the plurality of time instants. The load emission data is associated with the emission of the gas by the load.

In an embodiment, the power grid is connected to one or more power plants comprising the specific power plant. The program instructions cause the processor set to determine distribution data associated with the load based on an application of the fourth AI model on the grid topology data and the power consumption data. The distribution data indicates a distribution of a total amount of power consumed by the load over each power plant of the one or more power plants connected to the power grid. The program instructions cause the processor set to determine the load emission data based on an application of the fourth AI model on the distribution data and the fourth emission level associated with the emission of the gas by the specific power plant at each time instant of the plurality of time instants.

In an embodiment, the program instructions cause the processor set to generate the third emission level based on an application of a dispersion model. The dispersion model is calibrated based on one or more environmental factors associated with the geographical region.

According to an embodiment of the present disclosure, a computer program product for estimation of emission levels associated with emission of a gas by power plants is described. The computer program product includes one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media to perform operations including receiving image data associated with a plurality of images of a geographical region. The geographical region includes a specific power plant. The image data indicates emission data associated with the gas. The operations further include generating a first emission level associated with the gas based on an application of a first artificial intelligence (AI) model on the image data. The first emission level indicates a total amount of the gas present in the geographical region. The operations further include generating a second emission level based on an application of a second AI model on the first emission level, the second emission level is associated with an emission of the gas by the specific power plant within a first predefined time period. The operations further include generating a third emission level based on the second emission level. The third emission level is associated with an emission of the gas by the specific power plant within each time period of a plurality of second predefined time periods. The first predefined time period includes the plurality of the second predefined time periods. The operations further include determining calibration factor data based on an application of a third AI model on the third emission level associated with each time period of the plurality of second predefined time periods, the calibration factor data is associated with the emission of the gas by the specific power plant within the first predefined time period. The calibration factor data includes a plurality of calibration values that correspond to a plurality of time instants within the first predefined time period. The operations further include estimating a fourth emission level based on the calibration factor data. The fourth emission level is associated with the emission of the gas by the specific power plant at each time period of the plurality of time instants.

Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated operation, concurrently, or in a manner at least partially overlapping in time.

A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer-readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random-access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits/lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer-readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation, or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.

1 FIG. 100 120 120 100 102 104 106 108 110 112 102 114 114 114 116 118 120 120 120 122 122 122 122 124 108 108 110 110 110 110 110 110 is a diagram that illustrates a computing environmentfor determination of real-time emission levels of power plants, in accordance with an embodiment of the disclosure. The diagram contains an exemplary environment for execution of at least one module involved in performing the methods, such as a real-time emission determination moduleB associated with the automated tagging of the service requests. In addition to the real-time emission determination moduleB, computing environmentincludes, for example, a computer, a wide area network (WAN), an end-user device (EUD), a remote server, a public cloud, and a private cloud. In this embodiment of the disclosure, the computerincludes a processor set(including a processing circuitryA and a cacheB), a communication fabric, a volatile memory, a persistent storage(including an operating systemA and the real-time emission determination moduleB, as identified above), a peripheral device set(including a user interface (UI) device setA, a storageB, and an Internet of Things (IoT) sensor setC), and a network module. The remote serverincludes a remote databaseA. The public cloudincludes a gatewayA, a cloud orchestration moduleB, a host physical machine setC, a virtual machine setD, and a container setE.

102 108 100 102 102 102 1 FIG. The computermay take the form of a desktop computer, a laptop computer, a tablet computer, a smartphone, a smartwatch or other wearable computer, a mainframe computer, a quantum computer, or any other form of a computer or a mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as a remote databaseA. As is well understood in the art of computer technology, and depending upon the technology, the performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of the computing environment, detailed discussion is focused on a single computer, specifically the computer, to keep the presentation as simple as possible. The computermay be located in a cloud, even though it is not shown in a cloud in. On the other hand, computeris not required to be in a cloud except to any extent as may be affirmatively indicated.

114 114 114 114 114 114 114 114 114 The processor setincludes one, or more, computer processors of any type now known or to be developed in the future. The processing circuitryA may be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. The processing circuitryA may implement multiple processor threads and/or multiple processor cores. The cacheB may be memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on the processor set. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitryA. Alternatively, some, or all, of the cacheB for the processor setmay be located “off-chip.” In some computing environments, the processor setmay be designed for working with qubits and performing quantum computing.

102 114 102 114 114 100 120 120 Computer readable program instructions are typically loaded onto the computerto cause a series of operations to be performed by the processor setof the computerand thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the methods”). These computer-readable program instructions are stored in various types of computer-readable storage media, such as the cacheB and the other storage media discussed below. The program instructions, and associated data, are accessed by the processor setto control and direct the performance of the methods. In computing environment, at least some of the instructions for performing the methods may be stored in the real-time emission determination moduleB in persistent storage.

116 102 The communication fabricis the signal conduction path that allows the various components of computerto communicate with each other. Typically, this fabric is made of switches and electrically conductive paths, such as the switches and electrically conductive paths that make up buses, bridges, physical input/output ports, and the like. Other types of signal communication paths may be used, such as fiber optic communication paths and/or wireless communication paths.

118 118 102 118 102 118 102 The volatile memoryis any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, the volatile memoryis characterized by a random access, but this is not required unless affirmatively indicated. In the computer, the volatile memoryis located in a single package and is internal to computer, but alternatively or additionally, the volatile memorymay be distributed over multiple packages and/or located externally with respect to the computer.

120 102 120 120 120 120 120 120 The persistent storageis any form of non-volatile storage for computers that is now known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is being supplied to computerand/or directly to the persistent storage. The persistent storagemay be a read-only memory (ROM), but typically at least a portion of the persistent storageallows writing of data, deletion of data, and re-writing of data. Some familiar forms of the persistent storageinclude magnetic disks and solid-state storage devices. The operating systemA may take several forms, such as various known proprietary operating systems or open-source Portable Operating System Interface-type operating systems that employ a kernel. The real-time emission determination moduleB typically includes the at least one module involved in performing the methods.

122 102 102 122 122 122 122 102 102 122 The peripheral device setincludes the set of peripheral devices of computer. Data communication connections between the peripheral devices and the other components of computermay be implemented in various ways, such as Bluetooth connections, Near-Field Communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insertion-type connections (for example, secure digital (SD) card), connections made through local area communication networks and even connections made through wide area networks such as the internet. In various embodiments of the disclosure, the UI device setA may include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smartwatches), keyboard, mouse, printer, touchpad, game controllers, and haptic devices. The storageB is external storage, such as an external hard drive, or insertable storage, such as an SD card. The storageB may be persistent and/or volatile. In some embodiments of the disclosure, storageB may take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments of the disclosure where computeris required to have a large amount of storage (for example, where computerlocally stores and manages a large database) then this storage may be provided by peripheral storage devices designed for storing very large amounts of data, such as a storage area network (SAN) that is shared by multiple, geographically distributed computers. The IoT sensor setC is made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer, and another sensor may be a motion detector.

124 102 104 124 124 124 102 124 The network moduleis the collection of computer software, hardware, and firmware that allows computerto communicate with other computers through WAN. The network modulemay include hardware, such as modems or Wi-Fi signal transceivers, software for packetizing and/or de-packetizing data for communication network transmission, and/or web browser software for communicating data over the internet. In some embodiments of the disclosure, network control functions, and network forwarding functions of the network moduleare performed on the same physical hardware device. In various embodiments of the disclosure (for example, embodiments that utilize software-defined networking (SDN)), the control functions and the forwarding functions of the network moduleare performed on physically separate devices, such that the control functions manage several different network hardware devices. Computer-readable program instructions for performing the methods can typically be downloaded to computerfrom an external computer or external storage device through a network adapter card or network interface included in the network module.

104 104 104 The WANis any wide area network (for example, the internet) capable of communicating computer data over non-local distances by any technology for communicating computer data, now known or to be developed in the future. In some embodiments of the disclosure, the WANmay be replaced and/or supplemented by local area networks (LANs) designed to communicate data between devices located in a local area, such as a Wi-Fi network. The WANand/or LANs typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and edge servers.

106 102 102 106 102 102 124 102 104 106 106 106 The EUDis any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer) and may take any of the forms discussed above in connection with computer. The EUDtypically receives helpful and useful data from the operations of computer. For example, in a hypothetical case where computeris designed to provide a recommendation to an end user, this recommendation would typically be communicated from the network moduleof computerthrough WANto EUD. In this way, the EUDcan display, or otherwise present recommendations to an end user. In some embodiments of the disclosure, EUDmay be a client device, such as a thin client, heavy client, mainframe computer, desktop computer, and so on.

108 102 108 102 108 102 102 102 108 108 The remote serveris any computer system that serves at least some data and/or functionality to the computer. The remote servermay be controlled and used by the same entity that operates the computer. The remote serverrepresents the machine(s) that collect and store helpful and useful data for use by other computers, such as the computer. For example, in a hypothetical case where the computeris designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to the computerfrom the remote databaseA of the remote server.

110 110 110 110 110 110 110 110 110 110 110 104 The public cloudis any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages the sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of the public cloudis performed by the computer hardware and/or software of the cloud orchestration moduleB. The computing resources provided by the public cloudare typically implemented by virtual computing environments that run on various computers making up the computers of the host physical machine setC, which is the universe of physical computers in and/or available to the public cloud. The virtual computing environments (VCEs) typically take the form of virtual machines from the virtual machine setD and/or containers from the container setE. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after the instantiation of the VCE. The cloud orchestration moduleB manages the transfer and storage of images, deploys new instantiations of VCEs, and manages active instantiations of VCE deployments. The gatewayA is the collection of computer software, hardware, and firmware that allows public cloudto communicate through WAN.

Some further explanation of virtualized computing environments (VCEs) will now be provided. VCEs can be stored as “images.” A new active instance of the VCE can be instantiated from the image. Two familiar types of VCEs are virtual machines and containers. A container is a VCE that uses operating-system-level virtualization. This refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances, called containers. These isolated user-space instances typically behave as real computers from the point of view of programs running in them. A computer program running on an ordinary operating system can utilize all resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and devices assigned to the container, a feature which is known as containerization.

112 110 112 104 110 112 The private cloudis similar to public cloud, except that the computing resources are only available for use by a single enterprise. While the private cloudis depicted as being in communication with the WAN, in an embodiment of the disclosure, a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community, or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment of the disclosure, the public cloudand the private cloudare both part of a larger hybrid cloud.

2 FIG. 2 FIG. 1 FIG. 1 FIG. 1 FIG. 200 202 224 200 202 204 202 204 206 214 216 218 220 222 200 104 202 102 is a diagram that illustrates a network environmentin which a systemfor determination of a real-time emission level of a specific power plantis implemented, in accordance with an embodiment of the disclosure.is explained in conjunction with elements from. The network environmentincludes the system, a database. The systemincludes a first AI model, a second AI model, and a third AI model. The databaseincludes image data, a first emission level, a second emission level, a third emission level, calibration factor data, and a fourth emission level. The network environmentfurther includes the WANof. In an embodiment, the systemmay be an exemplary embodiment of the computerof.

202 224 202 202 220 202 202 The systemmay include suitable logic, circuitry, interfaces, and/or code that may be configured for the determination of real-time emission levels of the specific power plant. The systemutilizes advanced machine learning algorithms to process large datasets, allowing for real-time analysis of emissions data. Additionally, the systemgenerates the calibration factor datawhich is scalable to other power plants with similar profiles. By applying the calibration factors determined for one power plant to other power plants with comparable operational characteristics and environmental conditions, the systemprovides reliable emissions estimates for a broader range of facilities. Moreover, the system's dynamic modeling capabilities enable it to disaggregate emissions data into sub-daily intervals, capturing fluctuations in emissions throughout the day. This level of detail is utilized for understanding the operational impacts of the power plant and for identifying trends over time. Finally, the systemenhances stakeholder engagement by providing transparent and accessible emissions data. This transparency fosters accountability and supports efforts to reduce greenhouse gas emissions, contributing to broader climate change mitigation initiatives.

204 202 204 204 204 204 206 204 204 202 202 204 In an embodiment, the databasecorresponds to an organized collection of data that may be stored and accessed electronically from a computer system (such as the system). The databaseis configured to manage, store, retrieve, and update data efficiently. In an exemplary implementation, the structure of the databasetypically involves tables, records, and fields that can be managed through various database management systems (DBMS). Examples of the databaseinclude but are not limited to, a relational database, a Non-Structured Query Language (SQL) database, a hierarchical database, a network database, a transactional database, a data warehouse, and a distributed database. In an exemplary embodiment, the databaseis configured to store the image dataobtained from various sources, including, but not limited to, satellites and aerial vehicles, such as drones. This allows the databaseto collect and organize a wide range of geospatial data is utilized for analysis. Additionally, the databaseis configured to retain intermediate outputs generated by the system, such as results from each of the AI models utilized in the system. By storing these outputs, the databaseprovides efficient data management and retrieval, enabling seamless access to critical information for further analysis and decision-making. This structured storage approach enhances the system's ability to track changes over time, compare results across different models, and refine emissions estimates based on the latest data inputs.

208 206 214 208 206 208 214 208 208 224 208 The first AI modelanalyzes the image dataassociated with a geographical region and determines a total amount of gas, referred to as the first emission level, present in the geographical region. The first AI modelutilizes the image dataassociated with a plurality of images such as, but not limited to, satellite images, aerial photos, or any other visual or spectral data. Further, the first AI modelis configured to detect the first emission levelof the gas present in the geographical region. In an example, the first AI modelis a geospatial foundational model (GFM). The first AI modelutilizes various techniques such as, but not limited to, image segmentation, object detection, and feature extraction to identify the emission source i.e., the specific power plantin the plurality of images. Further, the first AI modelutilizes spectral analysis to measure the gas concentration based on a wavelength reflected or absorbed by the gas.

210 224 208 210 224 210 224 224 210 216 224 210 The second AI modelis configured to estimate the amount of gas that is being emitted by the specific power plantwithin the geographical region within a first emission time period. The first AI modelis configured to determine the amount of gas present in the geographical region and the second AI modelisolates the amount of emission of the gas in the geographical region with respect to the specific power plant. The second AI modelutilizes factors such as, but not limited to, a location of the specific power plant, the specific power plantoperation schedule, and other contextual factors for example, wind speed, and humidity in the geographical region. Further, the second AI modelis configured to determine the second emission levelassociated with the emission of gas by the specific power plant. The second AI modelutilizes various techniques such as, but not limited to, time-series forecasting, regression analysis, and spatiotemporal models.

212 212 222 220 212 218 224 220 212 212 The third AI modelutilizes various techniques such as, but not limited to, calibration algorithms, sensor fusion techniques, data correction techniques, and advanced regression models. The third AI modelis configured to estimate the emission, referred to as the fourth emission level, of the gas based on the calibration factor data. The third AI modelutilizes a combination of inputs, including the third emission levelof the emission of the gas from the specific power plant, the calibration factor data, profile data related to the plant's characteristics, and ground truth emission data. In an example, the third AI modelemploys algorithms to analyze these variables. Based on calibration values that correspond to various time instants, the third AI modeldetermines a detailed temporal analysis of the emission of the gas within the geographical region, for adjustments in future predictions based on real-world measurements.

224 In an embodiment, the geographical region represents a distinct area of land and/or a combination of land and water characterized by specific features that differentiate it from surrounding areas. These defining characteristics may be, for example, specific power plantboundary characteristics. The geographical region may correspond to a defined location on the Earth's surface, i.e., covering a specific spatial extent. The geographical region exhibits relative homogeneity in terms of shared characteristics, which can be either natural or human-made, and have boundaries that may be clearly defined or transitional, where the characteristics blend into other regions.

The term “power plant” refers to an industrial facility that generates electricity by converting various forms of energy into electrical power. The power plant provides the energy required for lighting, heating, cooling, and powering appliances and machinery in homes, businesses, and industries. Power plants typically include a power source, such as fossil fuels (coal, natural gas, or oil), nuclear energy, or renewable sources (solar, wind, hydroelectric, or geothermal), a generator that converts mechanical energy into electrical energy, a turbine driven by the power source's energy, and a control system that regulates and manages the plant's operations. The process of generating electricity in a power plant begins with the activation of the energy source. In fossil fuel plants, this involves burning coal or natural gas, while nuclear plants trigger controlled nuclear reactions. Renewable sources like sunlight or wind provide energy directly. The generated heat is then used to produce steam in a boiler or steam generator, which drives the turbine connected to the generator. As the turbine spins, it causes the generator's coil to rotate within a magnetic field, inducing a flow of electric current and converting mechanical energy into electrical energy. The power plants can be classified based on their energy source (fossil fuel, nuclear, or renewable), their size (small-scale or large-scale), or their duty (base load, intermediate load, or peak load).

202 206 206 224 206 224 In operation, the systemis configured to receive the image dataassociated with the plurality of images of the geographical region. In an example, the image datacorresponds to multi-spectral images of the geographical region. The image data may indicate information about a location, a shape, and characteristics of various features on the earth's surface, including landforms, infrastructure, and natural resources associated with the geographical region having the specific power plant. The image datais obtained through various mediums such as, but not limited to, satellite observation, aerial photography, or other imaging techniques that capture a visual or spectral representation of the geographical region. In an example, each of the plurality of images corresponds to an image of an atmosphere over the geographical region. Further, the geographical region comprises the specific power plant.

206 206 2 2 2 Moreover, the image dataindicates emission data associated with the gas. In an example, each of the plurality of images may be a multispectral image of the geographical region. The multispectral image is captured across a plurality of wavelength ranges in the electromagnetic spectrum, allowing for the extraction of information. Multispectral imaging employs several spectral bands, typically ranging from 4 micrometer (μm) to 15 me, to gather detailed data about the properties of the atmosphere in the geographical region at different wavelengths. These different wavelengths may indicate characteristics of different gasses present in the atmosphere in the geographical region. The multispectral images are captured using specialized cameras or sensors that separate light into distinct spectral bands, which can include visible light (0.4 to 0.7 μm), near-infrared (NIR; 0.7 to 1 μm), short-wave infrared (SWIR; 1 to 1.7 μm), mid-wave infrared (MWIR; 3.5 to 5 μm), and long-wave infrared (LWIR; 8 to 12 μm). The resulting image is captured as a series of monochrome grayscale images, with each image representing a specific wavelength range. The image dataindicates emission data associated with the gas. In an example, the gas may be, for example, carbon dioxide (CO), carbon monoxide (CO), sodium dioxide (SO), or nitrogen dioxide (NO). Subsequently, different spectral images corresponding to different spectrums may indicate emission levels of the different gases in the geographical region.

202 208 206 206 202 208 214 208 224 224 3 FIG.A In an example, the systemis configured to reconfigure the first AI modelbased on the image dataand a loss function associated with the image data. In an example, the systemutilizes a morphological loss function to fine-tune the pre-trained first AI modelfor determining the first emission levelof the gas in the geographical region. In certain cases, the first AI modelmay also determine profile data associated with the specific power plant. The profile data includes a type of power plant and a segmented area associated with the specific power plant. Further details associated with the profile data are described in conjunction with, for example,.

208 214 208 206 214 202 206 Further, the first AI modelis configured to generate a first emission levelassociated with the gas based on an application of the first AI modelon the image data. The first emission levelindicates the total amount of gas present in the geographical region. In an example, the systemutilizes advanced machine learning algorithms and geospatial analysis techniques, which interpret the image datato quantify the emissions of the gas in the geographical region. This process involves identifying specific emission sources, such as smokestacks and cooling towers, and tracking the dispersion of pollutants in the atmosphere.

202 208 202 214 202 2 2 2 Moreover, the systemintegrates the emission data with other contextual information, such as meteorological conditions and operational parameters, to enhance the accuracy and reliability of the emissions estimates. In an example the first AI modelis configured to determine the total amount of gas present in the geographical region. For example, the systemis configured to determine the first emission levelwith respect to COpresent in the atmosphere of the geographical region. Similarly, in other examples, the systemis configured to determine the total amount of emission by the other gases, such as SO, CO, and NO.

214 224 224 To this end, the first emission levelof the gas in the environment of the geographical region indicates a total amount of the gas present in the geographical region. Such a total amount of emission may be attributed to the historical accumulation of the gas within the environment. The present disclosure describes techniques for determining the real-time emission level of the specific power plantto accurately identify an amount of a gas released by the specific power plantin the environment in real-time.

202 216 224 214 202 210 216 202 224 202 216 210 216 224 224 2 The systemis configured to generate the second emission levelassociated with the emission of the gas by the specific power plantwithin a first predefined time period based on the first emission level. The systemutilizes the second AI modelto generate the second emission level. In an embodiment, the systemis configured to identify the amount of gas emitted by the specific power plantinto the atmosphere in the first predefined time period. The systemgenerates the second emission levelwithin the first predefined time period, such as, but not limited to, a day, such as in 24 hours. The output of the second AI modelis the second emission levelassociated with the emission of the gas by the specific power plantwithin the first predefined time period, for example, a daily emission of COgas by the specific power plant.

216 224 210 224 210 206 224 224 210 206 214 224 2 2 2 2 2 2 2 2 2 2 In an example the second emission levelcorresponds to the daily amount of COemitted by the specific power plantin the geographical region. It is crucial to note that once the COenters the atmosphere, it takes millions of years to decompose fully. Furthermore, various factors, including wind speed and humidity, can influence the accuracy of estimation of COemission on a daily basis. The second AI modelgenerates the daily emission of COby the specific power plant. The second AI modelutilizes the image dataassociated with other gases, such as, but not limited to, NOand SO, to identify the relative emission of COby the specific power plant. In an example, the amount of COemitted by the specific power plantin a single day is relatively small compared to the total amount of COpresent in the environment. To address this challenge, the second AI modelutilizes the image dataand the first emission levelto determine the emission of other gases and then uses the determined emission of other trace gases to calculate the relative amount of COemitted by the specific power plantin a single day.

202 218 216 218 224 202 218 224 202 218 224 216 202 218 224 216 218 2 2 4 FIG. Further, the systemgenerates the third emission levelbased on the second emission level. The third emission levelis associated with the emission of gas by the specific power plantwithin each of a plurality of the second predefined time periods. The first predefined time period includes the plurality of the second predefined time period. In an embodiment, the systemutilizes a dispersion model to generate the third emission levelassociated with the emission of the gas by the specific power plantwithin each of the plurality of second predefined time periods. In an example, the plurality of second predefined time periods corresponds to different time windows, such as 1 hour time windows within the first predefined time period, such as 24 hours or a day. In an example, the systemis configured to generate the third emission levelassociated with the emission of COby the specific power plantwithin a sub-daily time period, i.e., each hour of the day based on uniform distribution of the second emission levelthroughout the day. For instance, the systemmay utilize the dispersion model such as, but not limited to, the Gaussian plume model to generate the third emission levelof COemitted by the specific power plantin sub-daily time periods based on dividing the second emission levelover the first predefined time period, i.e., 24 hours. Details of the third emission levelare further described in conjunction with, for example,.

202 220 212 218 220 224 220 202 212 220 224 212 218 202 212 218 220 224 Further, the systemis configured to determine the calibration factor databased on an application of a third AI modelon the third emission levelassociated with each time period of the plurality of second predefined time periods. The calibration factor datais associated with the emission of the gas by the specific power plantat each of a plurality of time instants within the first predefined time period. In an example, the calibration factor datacomprises a plurality of calibration values that are continuously varying over each time instant of the first predefined time period. In an example, the systemutilizes the third AI modelto determine the calibration factor datafor accurate emission measurements from the specific power plant. The third AI modelprocesses data from the third emission levelover multiple predefined time periods. In other words, the systemutilizes the third AI modelto analyze the third emission levelfor different second predefined time periods within the first predefined time period. The outcome of this analysis is the calibration factor data, which consists of multiple calibration values corresponding to distinct time instants within the first predefined time period. Each of the multiple calibration values are utilized for adjusting and aligning the emission measurements, ensuring they reflect the true emission levels of the specific power plant.

202 216 218 216 224 202 216 218 224 224 218 202 220 220 According to an example embodiment, the systemis configured to distribute the second emission leveluniformly over the second predefined time periods within the first predefined time period to determine the third emission level. In an example, the second emission levelindicates emissions by a specific power plantin a day. Further, the systemis configured to distribute the second emission leveluniformly over each hour of a day to determine the third emission levelfor each hour of a day. However, the specific power plantmay not produce a same amount of energy or burn a same amount of fuel throughout the day. For example, the specific power plantmay be producing more energy in the morning between 10:00 AM and 12:00 PM and at night between 19:00 PM and 23:00 PM. As a result, the uniform distribution, i.e., the third emission level, fails to accurately identify an amount of emission at each time instant, say at 12:00 PM or 15:45 PM, etc. of the day. Subsequently, the systemis configured to determine the calibration factor data. The calibration factor datacomprises a plurality of dynamic calibration values that are continuously varying over each time instant of the plurality of time instants within the first predefined time period. For example, the plurality of dynamic calibration values may be a set of values that vary in each time instant, say every 1 minute, 5 minutes, 10 minutes, 20 minutes, 30 minutes, etc. over the day.

212 224 212 220 202 202 220 202 220 224 220 5 FIG.A In an example, the third AI modelanalyses various data inputs, including real-time emissions data, operational parameters, and environmental conditions, to produce a comprehensive assessment of gas emissions from the specific power plant. By leveraging advanced machine learning algorithms, the third AI modelidentifies patterns and correlations that inform the emissions data. The calibration factor dataserves as an adjustment parameter that accounts for variations in emissions due to different operational conditions and measurement uncertainties. For example, if the third emissions data reveals significant fluctuations during specific operational phases or under varying weather conditions, the systemadjusts the calibration factor accordingly. The systemcollects data from multiple sources, including historical emissions records and real-time monitoring data, to enhance the accuracy of the calibration factor data. By integrating this diverse dataset, the systemensures that the calibration factor datareflects the specific power plantemissions profile accurately. Details of the calibration factor dataare described in conjunction with, for example,.

202 220 218 212 220 218 In an exemplary embodiment, the systemis configured to generalize the calibration factor dataacross different power plants with similar profiles. Since emission levels are unique to each power plant, the third emission levelmeasured for one plant cannot be directly used to assess emission of different power plants. Instead, the third AI modeldetermines the calibration factor databased on the third emission leveland profile data of individual power plants. This data can then be applied to other power plants with comparable profiles, such as other power plants using the same type of fuel to produce electrical energy and/or producing the same or similar amount of energy, to achieve accurate emission level assessments.

202 222 220 222 224 202 222 224 220 222 202 224 Further, the systemis configured to estimate the fourth emission levelbased on the calibration factor data. The fourth emission levelis associated with the emission of the gas by the specific power plantat each of the plurality of time instants. In an embodiment, the systemestimates the fourth emission levelassociated with the emission of gas by the specific power plantat each of the plurality of time instants, using the calibration factor dataas a foundational input. Based on the fourth emission level, the systemadjusts the raw emissions estimates to reflect more accurately the actual emissions of the gas by the specific power plantat each time instant. This adjustment accounts for variations in operational efficiency, environmental factors, and measurement uncertainties that may influence the emissions data.

224 202 220 220 202 220 218 218 224 202 218 222 220 224 2 In an example, if the specific power plantoperates with varying efficiency levels throughout the day due to changes in demand and fuel quality. During peak hours, the plant may emit more gas due to higher energy production. The systemuses the calibration factor datato adjust the emissions estimates accordingly. If the calibration factor dataindicates that emissions are typically 20% higher during peak hours, the systemapplies the calibration factor datato the third emission level. As a result, if the third emission levelgenerated by the specific power plantfor a specific time instant is 100 tons of CO, the systemadjusts the third emission levelto the fourth emission levelto 120 tons by applying the calibration factor data. This refined estimate provides a more accurate representation of the specific power plantemissions at that time instant.

3 FIG.A 3 FIG.A 2 FIG. 300 214 208 202 is a diagram that illustrates a block diagramA of an exemplary operation for generating the first emission levelusing the first AI model, in accordance with an example embodiment of the present disclosure. In an example, the steps of the exemplary operation may be implemented by the system.is described in conjunction with elements of the.

202 206 204 206 In one embodiment, the systemis configured to receive the image datafrom the database. The image datais associated with the plurality of images. Moreover, each of the plurality of images corresponds to a multispectral image of the geographical region. Each of the plurality of images includes the emission data of the one or more gases in the geographical region. The plurality of images corresponds to the amount of gas present in the atmosphere, environment, or a vertical column of the geographical region.

206 302 302 2 In one embodiment, the image dataindicates location dataassociated with the plurality of images, and a timestamp associated with the plurality of images. In particular, the location dataeach pixel of each of the plurality of images includes graded pixel data of the geographical region and includes location data and the timestamp. Subsequently, each image of the plurality of images may indicate a geographical location associated with the geographical region as well as a timestamp at which the image was captured. In addition, each image of the plurality of images may indicate a spectrum of a certain wavelength. The wavelength may be chosen based on the type of the gas for which emission levels are checked. In an example, the spectrum of a first wavelength in the image may indicate distribution of COin the atmosphere of the geographical region. This allows for precise tracing of gas concentrations over specific areas and time periods. In an example, each pixel of the plurality of images corresponds to the area such as, but not limited to, 2 square kilometers (Sq Km) of the earth's surface.

2 In an embodiment, spectrum(s) of wavelength(s) in an image indicates emission data. The emission data includes an amount of gas present in the geographical region. The multispectral images capture data across specific wavelength ranges in the electromagnetic spectrum, typically using 4-15 bands, allowing for the extraction of the emission data for one or more types of gasses and location and/or temporal data associated with each pixel of each of the plurality of images. Each spectral band captures one or more gases present in the atmosphere of the geographical region. For instance, an image from the plurality of images provides information on the COconcentration in the atmosphere of the geographical region.

202 208 206 206 206 204 206 206 202 202 208 208 206 In an embodiment, the systemis configured to reconfigure the first AI modelbased on the image dataand a loss function associated with the image data. In an example, the received image datafrom databaseis having quality issues. Sensors experience issues related to, but not limited to, data quality, including missing data, cloud cover interference, and sensor calibration problems. These factors significantly impact the reliability of the received image data. For instance, in some cases, as much as 90% of the Image datamay not meet quality control standards, leaving only a small fraction of usable information. To address these challenges, the systemis configured to fill in the gaps left by missing or low-quality data. In one embodiment, the systemis configured to reconfigure the first AI model, which leverages machine learning techniques to analyze and extrapolate the available data. By creating a continuous data stack that is regular in both space and time, the first AI modelis configured to enhance the quality and usability of the image data.

202 208 206 208 302 206 224 224 208 In one embodiment, the systemreconfigures the first AI modelbased on the received image data. The first AI modelis reconfigured based on the location data, timestamps of the images, and the loss function associated with the image data. In an embodiment, the loss function is a morphological loss function. The morphological loss function is based on a structure of the specific power plantwithin the plurality of images. The morphological loss function is configured to recognize and leverage specific shapes and structural characteristics of industrial facilities, such as the specific power plantto reconfigure or fine-tune the first AI model. Different types of power plants exhibit unique shapes based on their fuel sources and operational designs, which can significantly influence their emissions profiles. For example, a coal-fired power plant may have a distinct layout compared to a natural gas facility, leading to variations in how emissions are released and dispersed.

208 206 224 208 208 208 In an embodiment, the morphological loss functions are specialized loss functions used in deep learning to enhance the performance of the first AI model. In particular, the morphological loss function is used in tasks involving shape and structure recognition. The morphological loss functions are designed to capture and utilize the unique morphological characteristics of objects within each of the plurality of images in the image dataand utilize the characteristics of objects for analyzing industrial facilities, such as the specific power plant. In the context of emissions monitoring, the morphological loss functions help the first AI modelto recognize distinct shapes associated with different types of power plants, which can vary based on their fuel sources and operational designs. For example, coal-fired power plants may have large smokestacks and expansive layouts, while natural gas plants may exhibit more compact designs. By incorporating these structural features into the modeling process, the first AI modelimproves the accuracy of emissions profiling. The morphological loss functions facilitate the segmentation of images, allowing the first AI modelto differentiate between various industrial facilities and assess their emissions more precisely.

202 208 304 224 206 304 224 224 202 304 224 206 302 208 224 206 304 202 224 202 224 224 224 202 202 202 202 The systemis configured to determine, using the first AI model, profile dataassociated with the specific power plantbased on the image data. The profile dataassociated with the specific power plantincludes at least one a type, or a segmented area within the plurality of images associated with the specific power plant. In an example, the systemis configured to determine profile dataassociated with the specific power plantin the geographical region by utilizing both image data, the location data, the timestamps and the loss function. In an example, the first AI modelgenerates detailed insights into the specific power plantcharacteristics and emissions based on the image data. The profile datagenerated by the systemincludes the type of power plant and the segmented area within the plurality of images associated with the specific power plant. For instance, the systemidentifies whether the specific power plantis coal-fired, natural gas, or renewable energy-based, such as a solar, hydro, or wind facility. This classification is needed for understanding the specific emissions profile and operational characteristics of the specific power plant. Additionally, the segmented area within the plurality of images provides spatial information about layout and structure of the specific power plant. By analyzing the plurality of images, the systemdelineates different sections of the facility, such as the turbine area, fuel storage, and emission stacks. The segmentation allows the systemfor a more granular analysis of emissions sources, enabling the identification of specific areas that may require monitoring or intervention. For example, if the systemprocesses images of a coal-fired power plant, the systemis configured to segment the areas associated with coal storage and combustion, providing insights into where emissions are likely to be highest.

208 214 202 2 2 In an exemplary embodiment, the first AI modelis configured to generate the first emission level. In an example, the systemgenerates XCO, which represents a column-average concentration of carbon dioxide, providing a measure of how much COis present in a vertical column of the atmosphere above the geographical region at a particular time of a day.

3 FIG.B 3 FIG.B 2 FIG. 3 FIG.A 300 306 202 is a diagram that illustrates a block diagramB of an exemplary operation for segmentation of a plurality of images, in accordance with an example embodiment of the present disclosure. In an example, the steps of the exemplary operation may be implemented by the system.is described in conjunction with elements of the, and.

202 306 306 224 306 224 224 306 In an exemplary embodiment, the systemis configured to receive the plurality of images. The plurality of imagesis associated with the geographical region and may include representation of the specific power plant. Each of the plurality of imagesmay provide unique information about gases present in the atmosphere around the specific power plantin the geographical region. However, in some instances, the boundaries of the specific power plantmay not be visible within the plurality of images. This lack of visibility can occur due to various factors, such as environmental conditions, obstructions, or the resolution of the images.

208 306 224 308 202 The first AI modelutilizes advanced image segmentation techniques to partition the plurality of imagesinto meaningful segments or regions. Each segmented area corresponds to specific characteristics, such as structural components of the specific power plantor the facility, a type of gas emitted, etc. For example, the segmentation may reveal distinct areas related to the coal storage, combustion units, and emission stacks of a coal-fired power plant. By analyzing the segmented area, the systemdetermines the emissions profile of the facility, identifying which areas contribute most significantly to greenhouse gas emissions.

302 202 308 224 208 308 224 308 The segmentation process also incorporates location data, allowing the systemto correlate the segmented area with specific geographical coordinates. The segmented arearefers to specific regions identified within images, particularly in the context of analyzing emissions from industrial facilities like the specific power plant. By utilizing the first AI model, the segmented areais delineated based on distinct features, such as structural components and emission sources. For instance, in a specific power plant, the segmented areamay include coal storage, combustion units, and emission stacks. This segmentation enables precise monitoring of gas emissions and facilitates targeted environmental assessments.

4 FIG. 4 FIG. 2 FIG. 3 FIG.A 3 FIG.B 400 218 202 is a diagram that illustrates a block diagramof an exemplary operation for generating the third emission level, in accordance with an example embodiment of the present disclosure. In an example, the steps of the exemplary operation may be implemented by the system.is described in conjunction with elements of the,and.

210 214 210 216 224 208 214 208 214 224 In one embodiment, the second AI modelis configured to receive the first emission levelassociated with the geographical region. The second AI modelis configured to generate the second emission levelassociated with the emission of the gas by the specific power plantwithin the first predefined time period. In an example, the first AI modelgenerates the first emission levelassociated with the geographical region. For instance, the first AI modelgenerates the first emission levelassociated with the specific power plant.

210 214 214 208 224 210 214 216 224 208 210 224 2 In one embodiment, the second AI modelis configured to receive the first emission levelassociated with a geographical region. This first emission levelis generated by the first AI model, which analyzes the emissions data and provides insights into the gas emissions from the specific power plant. The second AI modelthen utilizes the first emission levelto generate the second emission levelwhich reflects the emissions of gas by the specific power plantwithin a first predefined time period. For instance, if the first AI modeldetermines the initial emission level of COin the geographical region on a day. Further, the second AI modelprocesses this information to calculate the total emission of gas by the specific power plantassociated with the geographical region within the first predefined time period, such as, but not limited to, on a particular day or in a week.

210 216 224 210 210 224 210 2 2 2 2 In an exemplary embodiment, the second AI modelis configured to generate the second emission levelassociated with the COby the specific power plantwithin a first predefined time period such as, but not limited to daily. In an example, the second AI modelis configured to estimate the concentration of COthat would be present in the atmosphere in the absence of any new emissions from specific sources. The second AI modeluses historical data, satellite observations, and atmospheric measurements to build a comprehensive picture of baseline COlevels emitted by the specific power plantin the first predefined time period, say a day. By providing this baseline, the second AI modelhelps in distinguishing the additional COemissions from localized sources.

210 210 2 2 2 2 2 2 2 2 In an example, the second AI modelquantifies the concentration of carbon dioxide (CO) in the atmosphere. The second AI modelis designed to account for the background levels of COthat exist in the environment before adding emissions from specific sources, such as power plants. The background emission model establishes a baseline for COlevels, which helps in isolating and measuring the additional COemissions from local sources. The background level of COlevels refers to the natural and pre-existing concentration of COin the atmosphere. The background level of COlevels is influenced by a variety of factors, including natural processes like respiration, decay, and ocean-atmosphere exchanges, as well as human activities such as deforestation and industrial emissions. Since COhas a long atmospheric lifetime, it remains in the air for extended periods, making the background concentration stable over time but subject to seasonal and regional variations.

210 210 210 210 210 216 210 2 2 2 2 2 2 2 2 2 2 2 2 2 In one embodiment, the second AI modelintegrates a wide range of data sources, including historical COconcentrations, meteorological data (e.g., wind speed, temperature, humidity), and geographical information. Satellite observations are particularly valuable as they provide a global view of COdistributions and help in understanding the spatial and temporal variations in background levels. Calibration is crucial for ensuring the accuracy of the second AI model. This process involves adjusting the model parameters to match observed data. For example, if satellite data indicates higher COlevels in a specific region, the second AI modelis recalibrated to reflect these observations. In an embodiment, the second AI modelestimates the background level of COby analyzing historical data and current atmospheric conditions. It separates the baseline COconcentration from the variations caused by local emissions. This is done through statistical and computational methods that account for the natural variability in COlevels. Further, the second AI modelis configured to generate the second emission levelassociated with the emission of the gas by the power plant within the first predefined time period. In an example, once the background COlevels are established, the second AI modelcalculates the additional COemissions, referred to as delta XCO(ΔXCO). This is the difference between the observed COconcentration and the estimated background level. ΔXCOrepresents the contribution of local sources, such as power plants, to the overall COconcentration within the first predefined time period such as a daily time period.

202 218 216 218 224 218 404 216 404 218 216 404 216 404 Further, the systemis configured to generate the third emission levelbased on the second emission level. The third emission levelis associated with the emission of the gas by the specific power plantwithin each time period of a plurality of second predefined time periods. The first predefined time period comprises the plurality of the second predefined time periods. In an embodiment, the third emission levelis generated based on an application of a dispersion modelon the second emission level. In an example, the dispersion modelgenerates the third emission levelby simulating how pollutants disperse from a power plant based on the second emission level. The dispersion modelincorporates environmental factors such as wind speed, temperature, humidity, and the second emission levelto predict the concentration of emission of gas over time and distance. The dispersion modelis calibrated based on one or more environmental factors associated with the geographical region.

404 404 404 404 404 404 In an embodiment, the dispersion modelis a mathematical and computational tool used to simulate the spread and dispersion of gas in the atmosphere of the geographical region. The dispersion modelworks by solving mathematical equations that describe the physical processes governing environmental factors, such as gas transport, including advection, diffusion, and chemical reactions. The dispersion modelutilizes environmental factors such as meteorological conditions for example, but not limited to, wind speed, wind direction, temperature, and humidity significantly influence how gas disperses in the geographical region. For instance, high wind speeds can lead to greater dispersion and lower gas concentrations at ground level, whereas low wind speeds result in higher concentrations and localized pollution. Temperature inversions, where warmer air traps pollutants close to the ground, can also affect the dispersion patterns of the gas in the geographical region. The dispersion modelincorporates these variables to simulate realistic gas spread. The dispersion modelutilizes other factors such as, but not limited to, topography, or the physical features of the geographical region. For example, a valley might trap gas, leading to higher concentrations, whereas a mountainous region might cause pollutants to disperse more rapidly. The dispersion modeladjusts for these variations by incorporating data on local terrain.

404 404 The dispersion modelalso includes other factors such as the nature of emission sources, including their height, volume, and type of emissions, which also affect dispersion. Tall stacks release gas higher into the atmosphere, where they are more widely dispersed, while ground-level sources might result in more concentration of gas. The dispersion modeladjusts the predictions based on these characteristics to reflect the true dispersion behavior of the gas in the geographical region.

404 404 404 404 In an example, the calibration of the dispersion modelinvolves comparing the dispersion modelpredictions with actual observed data from monitoring stations. By fine-tuning the dispersion modelparameters based on this comparison, the dispersion modelreflects real-world conditions as closely as possible.

404 218 202 216 210 218 202 404 404 2 2 2 2 2 x 2 In an embodiment, the dispersion modelgenerates the third emission levelby applying these calibrated parameters. For example, consider a coal-fired power plant located in a coastal region, emitting gases such as CO, NO, and SO. The systemcalculates the second emission levelusing the second AI model, revealing emissions of 500 tons of CO, 50 tons of SO, and 30 tons of NOat the daily level. This time period is defined as one day, divided into sub-daily intervals for detailed analysis. To generate the third emission level, the systemapplies the dispersion model, which incorporates environmental factors like wind speed (10 mph), wind direction (southwest), temperature (60° F. to 75° F.), and humidity (70%). The dispersion modelpredicts emissions dispersion over the day, yielding results such as 20 tons of COin a sub-daily time period. The sub-daily time period may correspond to the second predefined time periods of, for example, one hour.

5 FIG.A 5 FIG.A 2 FIG. 3 FIG.A 3 FIG.B 4 FIG. 500 220 202 is a diagram that illustrates a block diagramA of an exemplary operation for determination of the calibration factor data, in accordance with an example embodiment of the present disclosure. In an example, the steps of the exemplary operation may be implemented by the system.is described in conjunction with elements of the,,, and.

202 304 224 212 304 208 304 308 304 In one embodiment, the systemis configured to input the profile dataassociated with the specific power plantto the third AI model. In an example, the profile datais generated by the first AI model. The profile datamay include a type of power plant, its operational characteristics, and the identified segmented area. The profile dataprovides a foundational understanding of the facility's emissions behavior, which is utilized for accurate modeling.

202 218 212 218 218 202 Further, the systemis configured to input the third emission levelwithin each time period of the plurality of second predefined time periods to the third AI model. The third emission levelis derived from a dispersion model, which simulates how pollutants disperse in the atmosphere based on various factors, including meteorological conditions and emission sources. The third emission levelis calculated for each time period within a predefined set of second time periods, allowing the systemto analyze emission trends over time. This temporal analysis is crucial for understanding how emissions fluctuate due to operational changes or environmental factors.

202 502 224 202 502 224 502 224 502 212 202 Further, the systemis configured to receive ground truth emission dataassociated with the specific power plant. In an example, the systemthen receives ground truth emission dataassociated with the specific power plant. The ground truth emission datais collected from direct measurements or reliable monitoring systems that provide actual historical emissions levels associated with the specific power plant. The ground truth emission dataserves as a benchmark for validating the predictions of the third AI model, ensuring that the systemaccurately assesses the performance of its emission estimates.

202 502 212 502 212 202 212 Further, the systemis configured to input the ground truth emission datato the third AI model. In an example, the ground truth emission dataallows the third AI modelto evaluate its predictions against the actual emissions recorded. By incorporating this real-world data, the systemidentifies discrepancies between predicted and observed emissions, which is utilized for refining the third AI model.

202 220 212 304 218 502 212 220 224 220 404 222 224 Further, the systemis configured to determine the calibration factor databased on the application of the third AI modelon the profile data, the third emission level, and the ground truth emission data. In an example, using these three inputs, the third AI modelapplies statistical analysis to calculate the calibration factor data, which adjusts the predicted emissions of the gas by the specific power plantto align more closely with the actual observed values. This calibration factor datais used for refining the emissions estimates produced by the dispersion model, enhancing their accuracy and reliability. The fourth emission levelis a more precise emissions level associated with the emission of gas by the specific power plant.

220 222 224 220 202 Furthermore, the calibration factor datais used to generate the fourth emission level, associated with the gas emissions from another specific power plant. By applying the calibration factor datato the emissions data of this different facility, the systemadjusts the predicted emissions to ensure they accurately reflect the actual gas emissions.

5 FIG.B 5 FIG.B 1 FIG. 2 FIG. 3 FIG.A 3 FIG.B 4 FIG. 5 FIG.A 500 202 is a diagram that illustrates a method flow diagramB that depicts the estimation of emission data associated with the emission of gas by each of a set of power plants, in accordance with an embodiment of the disclosure. In an example, the steps of the exemplary operation may be implemented by the system.is explained in conjunction with elements from,,,,, and.

504 202 224 202 202 224 302 202 At, power plant data associated with each of a plurality of power plants is received. In one embodiment, the systemis configured to receive power plant data associated with each power plant of the plurality of power plants. For example, each power plant of the plurality of power plants is associated with a geographical location. The power plant data comprises imagery data of the geographical location associated with each power plant of the plurality of power plants. In one embodiment, the plurality of power plants is inclusive or exclusive of the specific power plant. In an example, consider a scenario where the systemreceives power plant data associated with the plurality of power plants, for example, a first coal-fired power plant, a second coal-fired power plant, a third coal-fired power plant, a fourth natural gas plant, and a fifth solar power plant. The imagery data for each plant may reveal distinct features. The coal-fired plant might show large coal storage areas and emissions stacks, while the natural gas facility could display gas turbines and a more compact layout. In contrast, the solar power plant would be characterized by extensive solar panels and minimal emissions. By analyzing this imagery data, the systemgathers information about the geographical context of each power plant, including surrounding infrastructure, land use, and potential environmental impacts. This information is utilized for identifying similarities and differences between the specific power plantand the plurality of power plants. Additionally, the location dataallows the systemto consider local meteorological conditions, terrain features, and regulatory requirements that may affect emissions.

506 202 304 202 304 224 At, a set of power plants from the plurality of power plants is identified. In one embodiment, the systemis configured to identify a set of power plants from the plurality of power plants based on at least one similarity criterion between the power plant data associated with each power plant of the plurality of power plants and the profile data. In an example, the systemis configured to identify a set of power plants from the plurality of power plants based on a similarity of the profile dataassociated with the specific power plantand the power plant data associated with the plurality of power plants.

304 224 224 308 224 202 304 202 304 202 224 304 202 For example, the profile dataassociated with the specific power plantshows that the type of the specific power plantis a coal-fired power plant and the segmented areais associated with the specific power plant. Further, the systemis configured to identify the set of power plants based on the profile databeing similar to the power plant data. For instance, the systemidentifies that the second coal-fired power plant and the third coal-fired power plant have similar power plant data as the profile dataowing to a same type of fuel, i.e., coal, being used for generating energy. Subsequently, the set of power plants may include the second coal-fired power plant and the third coal-fired power plant. In this example, the systemdetects that both the second coal-fired power plant and the third coal-fired power plant share similar characteristics and operational data with the specific power plantassociated with the profile data. This similarity may encompass aspects such as emissions patterns, operational efficiency, type of fuel, an amount of energy being generated, and geographical location. As a result, the systemcreates a set of power plants that includes the second coal-fired power plant and the third coal-fired power plant.

508 220 202 220 202 220 224 304 220 224 304 At, emission data associated with the emission of the gas by each power plant of a set of power plants is estimated based on the calibration factor data. In an embodiment, the systemis configured to estimate the emission data associated with the emission of the gas by each power plant of the set of power plants based on the calibration factor data. In an example, the systemutilizes the determined calibration factor datafor the specific power plantassociated with the profile datato estimate the emission data associated with the emission of the gas by the set of power plants. For instance, the calibration factor dataof the specific power plantassociated with the profile datais used to estimate the emission data from the second coal-fired power plant and/or the third coal-fired power plant.

5 FIG.C 5 FIG.A 2 FIG. 3 FIG.A 3 FIG.B 4 FIG. 5 FIG.A 5 FIG.B 500 220 202 is a diagramC that illustrates an exemplary operation for determination of the calibration factor datafor one or more sets of power plants, in accordance with an example embodiment of the present disclosure. In an example, the steps of the exemplary operation may be implemented by the system.is described in conjunction with elements of the,,,,, and.

202 202 208 512 510 516 514 510 510 510 510 510 308 510 202 202 202 208 516 514 514 3 FIG.A The systemis configured to analyze the power plants in greater detail by identifying specific sets based on their similarities. In this regard, the system, specifically the first AI model, is configured to determine first profile dataassociated with a first set of power plants, and second profile dataassociated with a second set of power plants. Each power plant within the first set of power plantsexhibits comparable profile data, for example, the profile data associated with the each power plant of the first set of power plantare associated with similar type (e.g., coal-fired power plant, natural gas power plant, or renewable power plant, producing a same or similar amount of energy, operating at similar efficiency, etc.). Similarly, the profile data associated with each power plant of the first set of power plantmay indicate a segmented area corresponding to the each power plant in the first set of power plants. For example, such segmented area of each power plant in the first set of power plantsmay be similar to the segmented area.For example, the first set of power plantsmay consist of three coal-fired facilities located in three geographical locations. By analyzing their imagery data and operational characteristics, the systemidentifies that they have comparable emissions sources, such as coal storage areas and combustion units. This similarity in profile data allows the systemto treat these power plants as a cohesive group for further analysis. Similarly, the system, specifically the first AI model, determines the second profile dataassociated with the second set of power plants. The second set of power plantsincludes power plants of a different type, such as natural gas power plants, which share comparable profile data among themselves. Details of determining profile data of a power plant are described in conjunction with, for example,.

202 212 220 212 518 510 512 212 520 514 516 5 FIG.A Once the sets of power plants have been identified based on their profile data, the system, specifically the third AI model, is configured to determine the calibration factor datafor at least one power plant within each set of the sets of power plants. Pursuant to the present example, the third AI modelis configured to determine first calibration factor dataassociated with a power plant in the first set of the power plantsbased on the first profile data. Similarly, the third AI modelis configured to determine second calibration factor dataassociated with a power plant in the second set of the power plantsbased on the second profile data. These calibration factor data serve as a reference point for adjusting emissions estimates to align more closely with actual observations. Details associated with determining calibration factor data based on profile data are described in conjunction with, for example,.

202 212 518 510 520 514 202 Finally, the system, specifically the third AI model, utilizes the respective calibration factor data to determine the emission data for each power plant within its corresponding set of power plants. The first calibration factor datais applied to each power plant in the first set of power plantsto estimate their emissions levels, while the second calibration factor datais used for each power plant in the second set of power plants. By applying the appropriate calibration factor to each set of power plants, the systemensures that the emissions estimates are tailored to the specific characteristics of the power plants, enhancing the accuracy of the predictions.

518 510 518 518 In an example, the first calibration factor datamay be applied to estimate emissions from a particular power plant in the first set of power plants. In such a case, the first calibration factor datamay be updated or fine-tuned based on specific profile data associated with the particular power plant. In an example, the first calibration factor datamay be used for estimating the emissions of the particular power plant accurately.

6 FIG.A 6 FIG.A 2 FIG. 1 FIG. 2 FIG. 600 202 610 604 600 202 204 200 104 202 602 204 606 600 604 300 200 is a diagram that illustrates a network environmentA in which the systemis implemented for determination of load emission dataassociated with a load, in accordance with an embodiment of the present disclosure.is described in conjunction with. The network environmentA includes the systemand the database. The network environmentmay further include the WANof. The systemincludes a fourth AI model. The databasemay be configured to store the grid topology data. The network environmentA further includes a load. In an embodiment, the network environmentmay be an exemplary embodiment of, or may be connected to the network environmentof.

606 204 606 606 606 606 606 In one embodiment, the grid topology datais stored in the database. the grid topology dataincludes a detailed structure and connections within an electrical power grid, utilized for understanding and managing electricity flow. The grid topology dataincludes information of components such as, but not limited to, nodes, which represent generators, i.e., power plants, loads, and substations, and edges, which are the transmission lines linking these nodes. The grid topology dataincludes transmission line parameters including conductance, susceptibility, impedance, and capacity, for determining how power is transmitted across the grid. Additionally, the grid topology dataincludes information of the bus, which serves as junction points where multiple lines or generators connect, and switches and transformers manage electrical flow and voltage levels. The grid topology datais used for power flow analysis, which helps identify how electricity moves from generators to consumers and optimizes network performance.

606 604 ij In one embodiment, a power flow solver may be used to analyze and utilize the grid topology datato determine electricity flow to the load. The power flow solver is an analytical tool that may be used to analyze and manage the flow of electrical power through the power grid. The power solver calculates the steady-state operating conditions of the grid by processing inputs such as grid topology, generation capacities, load demands, and transmission line characteristics like resistance and reactance. The power solver utilizes mathematical methods, such as, but not limited to, a Newton-Raphson method, to iteratively refine estimates of voltage levels and power flows until a stable solution is reached. Other methods, such as the Gauss-Seidel or Fast Decoupled method, may also be used depending on the specific needs and efficiency requirements. The output from a power flow solver provides critical insights, including voltage levels at various nodes, real and reactive power flows through transmission lines, and energy losses due to line resistance. The power flow in a transmission line Lis given by:

ij Pindicates real power flow on the line between bus ‘i’ to bus ‘j’, ij Xindicates impedance between the bus ‘i’ to bus ‘j’, i Vindicates voltage at bus ‘i’, j Vindicates the voltage at bus ‘j’, ij Gindicates conductance of the transmission line between the bus ‘i’ to bus ‘j’, ij Bindicates the susceptance of the transmission line between the bus ‘i’ to bus ‘j’, and ij θindicates the phase angle difference between buses i & j. where,

602 224 602 224 602 The fourth AI modelutilizes analytical tools designed to trace and allocate emissions of gas from the specific power plantto end consumers within a power grid. The fourth AI modeldetermines how emissions of gas from various generators, i.e., the specific power planttranslate into the energy consumed by different loads, such as commercial buildings or industrial facilities. The fourth AI modelanalyzes grid topology, which maps the connections between power plants and consumer loads, including the physical properties of transmission lines. This topology helps in understanding how power flows from generators to consumers.

602 604 602 The fourth AI modelis configured to determine the distribution of power, using the power flow solver. The power flow solver is configured to calculate how much power each generator supplies to each load, considering factors such as generation capacities and loaddemands. This data is used for accurate emission allocation. Each generator's emission profile is established based on its technology, fuel type, and operational efficiency, providing a basis for quantifying emissions produced. With both power flow and emission profiles in hand, the fourth AI modelestimates emissions to specific loads based on the proportion of energy they receive from different generators.

202 610 606 608 602 602 606 604 602 608 604 604 608 602 602 604 602 610 604 In an embodiment, the systemis configured to determine load emission databy integrating grid topology dataand power consumption datausing the fourth AI model. The fourth AI modelmay utilize the grid topology datato map the connections between the one or more power plants and consumer loads, such as the load. The mapping may also include transmission lines and their characteristics like conductance and impedance. Further, the fourth AI modelutilizes the power consumption data, reflecting the electricity usage of the loadat different time instants and the fourth emission levels of each of the one or more power plants to accurately determine the emission profile of the load. In an example, the power consumption datais gathered through energy meters installed at consumer sites. The fourth AI modelprocesses this information, integrating grid topology with power consumption and the fourth emission levels of each power plant. The fourth AI modelperforms power flow analysis to determine how much power the loadreceives from different generators, and then allocates emissions based on these power flows. The fourth AI modeloutputs the load emission dataat each time instant of the plurality of time instant. The result is a comprehensive set of load emission data, which specifies the gas emissions associated with the loadat each time instant.

6 FIG.B 6 FIG.B 1 FIG. 2 FIG. 3 FIG.A 3 FIG.B 4 FIG. 5 FIG.A 5 FIG.B 6 FIG.A 600 610 604 202 is a diagram that illustrates a method flowB for determination of the load emission dataassociated with the load, in accordance with an embodiment of the disclosure. In an example, the steps of the exemplary operation may be implemented by the system.is explained in conjunction with elements of,,,,,,, and.

612 606 202 606 224 222 606 202 At, the grid topology dataassociated with a power grid is received. In one embodiment, the systemis configured to receive the grid topology dataassociated with the power grid. The power grid is supplied by at least the specific power plantfor which the fourth emission levelis determined. In an example, the grid topology dataprovides a detailed map of the power grid's structure, including a layout of nodes and edges of the power grid. The nodes represent critical components such as power plants, substations, and consumer loads, while the edges denote the transmission lines connecting the nodes. For instance, the systemspecifically receives information about how the power grid is organized and interconnected, including the locations and capacities of the power plants that supply electricity to the grid.

614 202 202 202 At, power flow data associated with the power grid is determined. In one embodiment, the systemis configured to determine the power flow data associated with the power grid. The power flow data is used for understanding how electricity moves through the power grid, providing insights into the power distribution and power consumption of power across various nodes. In one embodiment, the systemcollects real-time data from sensors and smart meters located throughout the grid, which monitor voltage levels, current flow, and power consumption at different points. For example, the systemanalyzes the power flow data from a network of substations, transformers, and distribution lines to assess the flow of electricity from power generation sources to end users. The power flow data reveals patterns in energy usage, such as peak demand periods when consumption is highest.

616 608 604 202 608 604 604 608 604 604 202 608 608 202 604 At, the power consumption dataassociated with the loadis received. In one embodiment, the systemis configured to receive the power consumption dataassociated with the load. The loadis connected to the power grid. The power consumption datais used for understanding how much electricity is being consumed by the loadat various time instants. For example, the loadmay be a residential load, a commercial load, or an industrial load. For example, consider a scenario where an industrial facility is connected to the power grid. The systemcollects the power consumption datafrom smart meters installed in the industrial facility, which continuously monitors and reports energy usage in real-time. In this case, the power consumption datamight indicate that the industrial facilities consume an average of 20000 kilowatts during peak hours and 10000 kilowatts during off-peak hours. This information allows the systemto analyze consumption patterns, identify peak demand periods, and assess the loadon the power grid.

618 604 224 202 604 602 606 608 604 202 604 602 606 608 604 606 608 602 604 At, the distributed data associated with the loadis determined. In one embodiment, the power grid is connected to one or more power plants comprising the specific power plant. Further, the systemis configured to determine the distribution data associated with the loadbased on an application of the fourth AI modelon the grid topology dataand the power consumption data. The distribution data indicates a distribution of a total amount of power consumed by the loadover each power plant of the one or more power plants connected to the power grid. In an example, the systemdetermines the distribution data associated with the loadconnected to the power grid. This process involves applying the fourth AI modelto analyze the grid topology dataand the power consumption data. The distribution data provides insights into how the total amount of power consumed by the loadis allocated across each of the power plants connected to the power grid. For example, consider a scenario where the industrial facility is supplied with electricity from three power plants, for example, a first power plant, a second power plant, and a third power plant. The grid topology datareveals the connections and capacities of each of the three power plants, while the power consumption dataindicates that the total demand from the industrial facility is 20000 kilowatts (KW). The fourth AI modelprocesses this information and determines that the first power plant supplies 9000 KW, the second power plant supplies 6000 KW, and the third power plant supplies 5000 KW to the load.

The distribution data indicates that the first power plant, being the closest to the industrial facility and having the highest capacity, meets the largest share of the demand. The second power plant follows, and the third power plant provides the remaining supply.

620 610 202 610 602 606 608 222 224 610 604 202 610 602 606 608 222 606 608 222 602 604 604 At, the load emission datais determined. In an embodiment, the systemis configured to determine the load emission databased on the application of the fourth AI modelon the grid topology data, the power consumption data, and the fourth emission levelassociated with the emission of the gas by the specific power plantat each time instant of the plurality of time instants, The load emission datais associated with the emission of the gas by the load. In an example, the systemis configured to determine the load emission databased on the application of the fourth AI model, which processes the grid topology data, the power consumption data, and the fourth emission levelassociated with the emissions of the gas by the power plants at each time instant within a defined set of time intervals. The grid topology dataprovides insights into how power is distributed across the network, while the power consumption datareflects the energy usage patterns of various loads connected to the power grid. The fourth emission levelindicates the total emissions resulting from the power plants at each time instant of the plurality of time instants. By integrating these data sources, the fourth AI modelcan assess the emissions associated with the load, quantifying how much gas is emitted as a result of the energy consumed by the load.

202 610 602 222 224 202 610 602 222 604 222 602 In an embodiment, the systemis configured to determine the load emission databased on an application of the fourth AI modelon the distribution data and the fourth emission levelassociated with the emission of the gas by the specific power plantat each time instant of the plurality of time instants. In an example, the systemis configured to determine the load emission databased on the application of the fourth AI modelto the distribution data and the fourth emission level. The process is used for understanding the emissions generated by the loadconnected to the power grid, enabling better management of environmental impacts. Consider an industrial facility relying on electricity from three power plants. The fourth emission levelindicates the total emission of gas produced by each of the three power plants at each time instant of the plurality of time instants, while the distribution data, derived from the fourth AI model, shows how much power the industrial facility consumes and how this consumption is distributed across the three power plants.

604 222 602 610 610 202 604 2 2 2 2 2 2 2 According to an example, a total power consumption of the loadmay be 2000 kW at a first time instant, with the first power plant supplying 900 KW at the first time instant, the second power plant supplying 600 KW at the first time instant, and the third power plant supplying 500 KW at the first time instant. The fourth emission levelshows that the first power plant emits 1,200 tons of COat the first time instant, the second power plant emits 800 tons of COat the first time instant, and the third power plant emits 300 tons of COat the first time instant. Using this data, the fourth AI modelcalculates the load emission databy determining each power plant's emissions contribution to the overall load. For instance, the load emission datafor the industrial facility can be calculated as 540 tons of COfrom the first power plant at the first time instant, 240 tons of COfrom the second power plant at the first time instant, and 200 tons of COfrom the third power plant at the first time instant, totaling 980 tons of COat the first instant. This detailed analysis allows the systemto provide insights into the emissions generated by the load, enabling the industrial complex to implement targeted strategies for emissions reduction, optimize energy usage, explore renewable energy options, and enhance compliance with environmental regulations, supporting sustainable practices within the industrial sector.

7 FIG.A 7 FIG.A 2 FIG. 3 FIG.A 3 FIG.B 4 FIG. 5 FIG.A 5 FIG.B 5 FIG.C 6 FIG.A 6 FIG.B 700 222 224 202 is a block diagram thatA illustrates an exemplary operation for the generation of fourth emission levelassociated with the emission of the gas by the specific power plant, in accordance with an example embodiment of the present disclosure. In an example, the steps of the exemplary operation may be implemented by the system.is described in conjunction with elements of,,,,,,,and.

202 206 302 206 224 302 208 206 302 214 304 224 214 224 224 202 224 208 3 FIG.A 3 FIG.B In one embodiment, the systemreceives image dataand location datafrom one or more sources. The image dataencompasses the geographical region where the specific power plantis situated, while the location dataincludes the coordinates of the geographical region. The first AI modelprocesses the image dataand the location datato output the first emission leveland the profile dataassociated with the specific power plant. The first emission levelindicates the total amount of gas present in the geographical area, providing a quantitative measure of emissions. Additionally, the specific power plantprofile includes information, such as a type of power plant, and a segmented area associated with the specific power plant. The detailed operational data enables the systemto assess emissions accurately and understand the environmental context of the specific power plant. The detailed operation of the first AI modelis described in conjunction with theand.

210 216 224 210 224 214 210 210 224 Further, the second AI modelis configured to determine the second emission levelassociated with the emission of the gas by the specific power plantin the first predefined time period. The second AI modeldetermines the amount of gas emitted by the specific power plantin the first predefined time period based on the first emission level. The second AI modelis configured to determine the background level of gas in the atmosphere, i.e., an amount of gas present in the atmosphere before the first predefined time period, say a day under consideration. Thereafter, the second AI modeldetermines the second emission level, i.e., the amount of gas emitted by the specific power plantwithin the first predefined time period, for example, the day.

210 210 224 210 216 224 210 4 FIG. In an example, the second AI modelfirst assesses the background level of gas present in the atmosphere, which serves as a baseline for comparison. By understanding the ambient concentrations of gases, the second AI modelaccurately isolates the emissions attributable to the specific power plant. Once the background levels are established, the second AI modelgenerates the second emission levelassociated with the emission of the gas by the specific power plantin the first predefined time period. This process involves analyzing various data inputs, such as real-time monitoring data, historical emissions data, and environmental factors that may influence gas dispersion. The detailed operation of the second AI modelis described in conjunction with.

404 218 216 218 224 218 224 216 404 404 4 FIG. Thereafter, the dispersion modelis configured to determine the third emission levelbased on the second emission level. The third emission levelis associated with the emission of the gas by the specific power plantwithin each time period of a plurality of second predefined time periods. The first predefined time period comprises the plurality of the second predefined time period. The third emission levelis specifically associated with the emissions of gas from the specific power plantover each time period within a defined set of second predefined time periods. By utilizing the second emission levelas a foundation, the dispersion modelsimulates how the emitted gases disperse in the atmosphere, taking into account various environmental factors such as wind speed, temperature, and humidity. The detailed operation of the dispersion modelis described in conjunction with.

212 220 218 220 224 220 220 212 304 224 218 502 212 212 220 5 FIG.A Further, the third AI modelis configured to determine the calibration factor databy applying its algorithms to the third emission levelassociated with each time period of the plurality of second predefined time periods. This calibration factor dataprovides insights into the emissions of the gas by the specific power plantwithin the first predefined time period. The calibration factor dataincludes plurality of calibration values that correspond to various time instants within this predefined time period, allowing for a detailed temporal analysis of emissions. To generate the calibration factor data, the third AI modeltakes as input the profile datarelated to the specific power plant, the third emission level, and the ground truth emission data. By analyzing these inputs, the third AI modelproduces the calibration factor data that helps adjust the predicted emissions to align more closely with actual observed values. Details of the third AI modelto generate the calibration factor dataare described in conjunction with.

202 220 222 224 212 4 FIG. In various embodiments, the systemis configured to estimate a fourth emission level based on the calibration factor data, the fourth emission levelis associated with the emission of the gas by the specific power plantat each time instant of the plurality of time instants The detailed operation of the third AI modelis described in conjunction with.

7 FIG.B 7 FIG.A 2 FIG. 3 FIG.A 3 FIG.B 4 FIG. 5 FIG.A 5 FIG.B 5 FIG.C 6 FIG.A 6 FIG.B 7 FIG.A 700 610 604 202 is a block diagramB that illustrates an exemplary operation for the generation of the load emission dataassociated with the load, in accordance with an example embodiment of the present disclosure. In an example, the steps of the exemplary operation may be implemented by the system.is described in conjunction with elements of,,,,,,,,, and.

602 610 606 608 222 602 604 602 602 222 224 602 610 602 6 FIG.A 6 FIG.B In one embodiment, the fourth AI modelis configured to determine the load emission databy utilizing the grid topology data, the power consumption data, and the fourth emission level. The fourth AI modelanalyzes the interaction between the grid's structure and the power consumption patterns to assess the emissions associated with the load. By applying the fourth AI modelto the grid topology data, the fourth AI modelunderstands how energy flows through the network and identifies the emission contributions from various loads at each time instant within a defined set of time intervals. The fourth emission levelrepresents the emissions of gas by the specific power plantduring these intervals. By integrating this information, the fourth AI modelcalculates the load emission data, which quantifies the emissions produced by the energy consumed by the loads connected to the grid. This approach allows for a comprehensive assessment of emissions, linking power generation to consumption, and supporting more effective emissions management strategies. Detailed operation of the fourth AI modelis described in conjunction with, for example,and.

8 FIG. 8 FIG. 2 FIG. 3 FIG.A 3 FIG.B 4 FIG. 5 FIG.A 5 FIG.B 5 FIG.C 6 FIG.A 6 FIG.B 7 FIG.A 7 FIG.B 800 224 202 is a diagram that illustrates a flowchartof an exemplary method for estimating real-time emissions of a specific power plant, in accordance with an embodiment of the disclosure. In an example, the steps of the exemplary operation may be implemented by the system.is described in conjunction with elements of,,,,,,,,,, and.

802 202 206 306 224 206 At, image data associated with a plurality of images of a geographical region is received. In an embodiment, the systemis configured to receive the image dataassociated with the plurality of imagesof the geographical region. The geographical region includes a specific power plant. The image dataindicates emission data associated with a gas.

804 202 214 206 214 208 208 206 224 308 224 306 206 At, a first emission level associated with the gas based on the image data is generated. In an embodiment, the systemis configured to generate the first emission levelassociated with the gas based on the image data. The first emission levelis generated using the first AI model. In an example, the first AI modelis reconfigured using a loss function associated with the received image dataand labeled image data and outputs a specific power plantprofile comprising at least one of a type of the power plant, or the segmented areaof the specific power plantwithin the plurality of images, and the labeled image data comprises at least one labeled image associated with the received image data.

806 202 216 214 216 224 210 216 224 At, a second emission level associated with the emission of the gas is generated. In an embodiment, the systemis configured to generate the second emission levelbased on the first emission level. The second emission levelis associated with the emission of the gas by the specific power plantwithin a first predefined time period. In an example, the second AI modelgenerates the second emission levelfor the specific power plant.

808 202 218 216 218 224 404 218 At, a third emission level associated with the emission of the gas is generated. In an embodiment, the systemis configured to generate the third emission levelbased on the second emission level. The third emission levelis associated with an emission of the gas by the specific power plantwithin each of a plurality of second predefined time periods. The first predefined time period includes the plurality of the second predefined time periods. In an example, the dispersion modeldetermines the third emission levelassociated with the gas within each of the plurality of second predefined time periods.

810 202 220 218 220 224 220 202 220 212 At, calibration factor data associated with the emission of the gas is determined. In an embodiment, the systemis configured to determine the calibration factor databased on the third emission levelassociated with each time period of the plurality of second predefined time periods. The calibration factor datais associated with the emission of the gas by the specific power plantwithin the first predefined time period. The calibration factor dataincludes a plurality of calibration values corresponding to a plurality of time instants within the first predefined time period. In an example, the systemdetermines the calibration factor datausing the third AI model.

812 202 222 220 222 224 At, a fourth emission level is estimated. In an embodiment, the systemis configured to estimate the fourth emission levelbased on the calibration factor data. The fourth emission levelis associated with the emission of the gas by the specific power plantat each of the plurality of time instants.

202 Various embodiments of the disclosure may provide a non-transitory computer readable medium and/or storage medium having stored thereon, instructions executable by a machine and/or a computer to operate a system (e.g., the system) for real-time emission profiling of one or more industries. The instructions may cause the machine and/or computer to perform operations that include receiving, by a computer, image data associated with a plurality of images of a geographical region. The geographical region comprises a specific power plant, and the image data indicates emission data associated with a gas. The operations further include generating, using a first artificial intelligence (AI) model, a first emission level associated with the gas based on the image data. The operations further include generating, using a second AI model, a second emission level associated with an emission of the gas by the specific power plant within a first predefined time period based on the first emission level. The operations include generating a third emission level associated with an emission of the gas by the specific power plant within each of a plurality of second predefined time periods based on the second emission level. The first predefined time period comprises the plurality of the second predefined time period. The operations further include determining, a third AI model, calibration factor data associated with the emission of the gas by the specific power plant within the first predefined time period based on the third emission level associated with each of the plurality of second predefined time periods. The calibration factor data comprises a plurality of calibration values corresponding to a plurality of time instants within the first predefined time period. The operations further estimate a fourth emission level associated with the emission of the gas by the specific power plant at each of the plurality of time instants, based on the calibration factor data.

The descriptions of the various embodiments of the present disclosure have been presented for purposes of illustration but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.